Microsoft Fabric Data Engineer Associate DP-700 Practice Question
Your team needs to ingest telemetry from thousands of manufacturing devices that publish JSON events every second. You must archive the raw events in OneLake, route them into a Real-Time Analytics database, and create a Power BI report that refreshes with sub-second latency. The solution should be implemented by data engineers through a visual, no-code experience without writing PySpark or KQL. Which Microsoft Fabric streaming engine should you recommend?
KQL streaming ingestion in a Real-Time Analytics database
Eventstream
A custom Kafka consumer running on an Azure Kubernetes Service (AKS) cluster
Eventstream is the Fabric streaming engine that lets users build an end-to-end pipeline through a graphical designer. It can connect to sources such as Azure Event Hubs or MQTT, land the raw data in OneLake, and continuously ingest the same stream into a Real-Time Analytics (Kusto) database. Because Eventstream is configured completely through the UI, data engineers can meet the low-latency reporting and storage requirements without writing Spark code, KQL, or other custom logic. Spark Structured Streaming requires PySpark coding, and KQL streaming ingestion is configured with KQL commands rather than a drag-and-drop interface.
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Microsoft Fabric Data Engineer Associate DP-700
Ingest and transform data
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